code: def _find_predicted_neq_given_multilabel(
      labels: list, pred_probs: np.ndarray
  ) -> np.ndarray:
      """
  
      Parameters
       ----------
       labels : list
         List of noisy labels for multi-label classification where each example can belong to multiple classes
         (e.g. ``labels = [[1,2],[1],[0],[],...]`` indicates the first example in dataset belongs to both class 1 and class 2).
  
       pred_probs : np.ndarray
         Predicted-probabilities in the same format expected by the `~cleanlab.filter.find_label_issues` function.
  
       Returns
       -------
       label_issues_mask : np.ndarray
         A boolean mask for the entire dataset where ``True`` represents a
         label issue and ``False`` represents an example that is accurately
         labeled with high confidence.
  
      """
      y_one, num_classes = get_onehot_num_classes(labels, pred_probs)
      pred_neq: np.ndarray = np.zeros(y_one.shape).astype(bool)
      for class_num, (label, pred_prob_for_class) in enumerate(
          zip(y_one.T, pred_probs.T)
      ):
          pred_probs_binary = stack_complement(pred_prob_for_class)
          pred_neq[:, class_num] = find_predicted_neq_given(
              labels=label, pred_probs=pred_probs_binary
          )
      return pred_neq.sum(axis=1) >= 1
  
imports: import numpy
  from sklearn.metrics import confusion_matrix
  import multiprocessing
  import sys
  import warnings
  from typing import Any, Dict, Optional, Tuple, List
  from functools import reduce
  import platform
  from cleanlab.count import calibrate_confident_joint, num_label_issues, _reduce_issues
  from cleanlab.rank import order_label_issues, get_label_quality_scores
  import cleanlab.internal.multilabel_scorer
  from cleanlab.internal.validation import assert_valid_inputs
  from cleanlab.internal.util import value_counts_fill_missing_classes, round_preserving_row_totals, get_num_classes
  from cleanlab.internal.multilabel_utils import stack_complement, get_onehot_num_classes, int2onehot
  from cleanlab.typing import LabelLike
  from cleanlab.multilabel_classification.filter import find_multilabel_issues_per_class
label_desc: Use image node_id 6 for calling a global function with example usage: _find_predicted_neq_given_multilabel(labels, pred_probs) and returns: unknown